Periodic Reporting for period 5 - WELL-BEING (The dynamics underlying Well-being; Understanding the Exposome-Genome interplay)
Période du rapport: 2023-12-01 au 2024-09-30
The ERC Consolidator WELL-BEING project, led by Prof. Meike Bartels, aimed to transform our understanding of human well-being by bridging disciplinary boundaries and integrating insights from genetics, epigenetics, and environmental sciences. It addressed a critical issue: how biological and environmental factors interact to shape individual and societal well-being—a question of growing urgency as mental health and subjective quality of life gain traction in public policy.
The central challenge was the fragmented nature of existing research, which often treats genetic and environmental influences in isolation. For society, this gap limits the design of effective interventions and policies grounded in a holistic understanding of well-being across life stages and social contexts.
The project set four key objectives:
1. Define the well-being exposome by identifying and integrating static and dynamic environmental and social exposures.
2. Uncover the interplay between genome, epigenome, and exposome using polygenic scoring, structural equation modeling, and multivariate GWAS.
3. Develop a comprehensive framework for well-being by integrating multidisciplinary findings across datasets.
4. Innovate data collection through ecological momentary assessment (EMA), GPS/GIS, and social media text mining (SMTM) to capture real-time, context-sensitive data.
The project’s conclusions yielded major scientific and societal contributions. It introduced data-driven methods like Environment-Wide Association Studies (EnWAS) and Poly-Environmental Scores (PES), showing that subjective perceptions—e.g. perceived neighborhood safety—can influence well-being more than objective conditions. Over 300 genetic variants were identified as associated with well-being, and analyses demonstrated that genetic influence on well-being changes across the lifespan and differs partly from that on depressive symptoms.
In the final phase, advanced machine learning models were developed to predict well-being using psychosocial and neighborhood factors. These confirmed that environmental and psychosocial variables are stronger predictors than genetic data. Additionally, the COVID-19 pandemic provided an unforeseen opportunity to examine how well-being determinants shift under crisis, showing increased environmental and reduced genetic influence. Through methodological innovation, interdisciplinary integration, and real-world application, the WELL-BEING project significantly advanced the field and laid a foundation for future policy and research to enhance human flourishing.
The central challenge was the fragmented nature of existing research, which often treats genetic and environmental influences in isolation. For society, this gap limits the design of effective interventions and policies grounded in a holistic understanding of well-being across life stages and social contexts.
The project set four key objectives:
1. Define the well-being exposome by identifying and integrating static and dynamic environmental and social exposures.
2. Uncover the interplay between genome, epigenome, and exposome using polygenic scoring, structural equation modeling, and multivariate GWAS.
3. Develop a comprehensive framework for well-being by integrating multidisciplinary findings across datasets.
4. Innovate data collection through ecological momentary assessment (EMA), GPS/GIS, and social media text mining (SMTM) to capture real-time, context-sensitive data.
The project’s conclusions yielded major scientific and societal contributions. It introduced data-driven methods like Environment-Wide Association Studies (EnWAS) and Poly-Environmental Scores (PES), showing that subjective perceptions—e.g. perceived neighborhood safety—can influence well-being more than objective conditions. Over 300 genetic variants were identified as associated with well-being, and analyses demonstrated that genetic influence on well-being changes across the lifespan and differs partly from that on depressive symptoms.
In the final phase, advanced machine learning models were developed to predict well-being using psychosocial and neighborhood factors. These confirmed that environmental and psychosocial variables are stronger predictors than genetic data. Additionally, the COVID-19 pandemic provided an unforeseen opportunity to examine how well-being determinants shift under crisis, showing increased environmental and reduced genetic influence. Through methodological innovation, interdisciplinary integration, and real-world application, the WELL-BEING project significantly advanced the field and laid a foundation for future policy and research to enhance human flourishing.
From the outset, the WELL-BEING project pursued an ambitious, interdisciplinary research agenda to unravel the complex interplay of genetic, environmental, and psychosocial influences on well-being. Over the course of the grant, research was conducted across four main domains, delivering breakthroughs with wide-reaching implications.
To define the well-being exposome, the team launched one of the first EnWAS in the field, examining 139 environmental variables. Twenty-one were found significantly associated with well-being, particularly perceived safety and socioeconomic status—insights with real-world relevance for urban policy and mental health interventions. The development of Poly-environmental scores helped quantify the cumulative effects of these exposures, demonstrating that subjective environmental perceptions explained far more variance in well-being than objective conditions.
In parallel, the genetics workstream delivered a multivariate GWAS on the well-being spectrum, identifying over 300 genetic variants and improving polygenic prediction. Brain gene expression data linked these variants to regions such as the subiculum, enriching the biological understanding of well-being. Further findings showed strong genetic overlap between hedonic and eudaimonic well-being, though distinct environmental contributors also played a role.
The COVID-19 pandemic offered a unique natural experiment. Analyses revealed shifts in the balance of genetic and environmental influences, with environmental factors gaining prominence during crisis periods. These findings underscore the context-dependence of well-being determinants.
Innovative data collection methods thrived. EMA enabled real-time tracking of well-being fluctuations across days and seasons. SMTM provided cost-effective, scalable assessments of well-being from language data, validating its use as a complement to traditional surveys. A landmark machine learning model combining genomic and exposomic data confirmed that psychosocial and environmental factors are more predictive than genetic markers alone.
Dissemination was extensive, with more than 50 peer-reviewed publications and a strong presence at scientific and public forums. To ensure real-world impact, Prof. Bartels founded the Xplorit Foundation, a platform to translate research insights into strategies for building a society based on wellbeing.
To define the well-being exposome, the team launched one of the first EnWAS in the field, examining 139 environmental variables. Twenty-one were found significantly associated with well-being, particularly perceived safety and socioeconomic status—insights with real-world relevance for urban policy and mental health interventions. The development of Poly-environmental scores helped quantify the cumulative effects of these exposures, demonstrating that subjective environmental perceptions explained far more variance in well-being than objective conditions.
In parallel, the genetics workstream delivered a multivariate GWAS on the well-being spectrum, identifying over 300 genetic variants and improving polygenic prediction. Brain gene expression data linked these variants to regions such as the subiculum, enriching the biological understanding of well-being. Further findings showed strong genetic overlap between hedonic and eudaimonic well-being, though distinct environmental contributors also played a role.
The COVID-19 pandemic offered a unique natural experiment. Analyses revealed shifts in the balance of genetic and environmental influences, with environmental factors gaining prominence during crisis periods. These findings underscore the context-dependence of well-being determinants.
Innovative data collection methods thrived. EMA enabled real-time tracking of well-being fluctuations across days and seasons. SMTM provided cost-effective, scalable assessments of well-being from language data, validating its use as a complement to traditional surveys. A landmark machine learning model combining genomic and exposomic data confirmed that psychosocial and environmental factors are more predictive than genetic markers alone.
Dissemination was extensive, with more than 50 peer-reviewed publications and a strong presence at scientific and public forums. To ensure real-world impact, Prof. Bartels founded the Xplorit Foundation, a platform to translate research insights into strategies for building a society based on wellbeing.
The WELL-BEING project pushed the boundaries of well-being science by integrating multiple disciplines and data modalities. It challenged the fragmented, reductionist approaches of the past and instead pioneered holistic, data-rich methodologies.
A key breakthrough was applying EnWAS to well-being, shifting from narrow hypothesis-driven approaches to broad, data-driven discovery. The development of Poly-environmental scores (PES)—quantifying cumulative environmental effects—established a replicable framework for future exposome studies in the field.
In genetics, the team introduced novel multivariate GWAS techniques that identified more than 300 well-being-linked variants and significantly boosted polygenic prediction accuracy. This enabled detailed comparisons of hedonic and eudaimonic well-being at both phenotypic and genetic levels, revealing high genetic overlap but distinct environmental modulators.
The project also produced the largest epigenome-wide meta-analysis on well-being to date. Although it identified no epigenome-wide significant methylation sites, it provided foundational insights for future research and underscored the need for larger, harmonized epigenetic datasets.
The final phase focused on synthesizing these insights through machine learning amodeling. By integrating EMA, SMTM, and exposomic data, the project worked toward a dynamic, personalized model of well-being. These models aim to identify individualized well-being trajectories and inform tailored interventions.
Innovative uses of language data expanded the toolkit for measuring well-being, especially across diverse populations and languages. The validation of SMTM as a complementary approach to traditional surveys opens new pathways for real-time, large-scale well-being monitoring.
As a lasting contribution, the Xplorit Foundation ensures continued societal translation of the project’s findings, facilitating collaborations across sectors to promote well-being at scale.
In sum, the WELL-BEING project has advanced the science of human well-being by delivering groundbreaking methodologies, integrated datasets, and actionable insights—laying a durable foundation for future interventions, policy, and research.
A key breakthrough was applying EnWAS to well-being, shifting from narrow hypothesis-driven approaches to broad, data-driven discovery. The development of Poly-environmental scores (PES)—quantifying cumulative environmental effects—established a replicable framework for future exposome studies in the field.
In genetics, the team introduced novel multivariate GWAS techniques that identified more than 300 well-being-linked variants and significantly boosted polygenic prediction accuracy. This enabled detailed comparisons of hedonic and eudaimonic well-being at both phenotypic and genetic levels, revealing high genetic overlap but distinct environmental modulators.
The project also produced the largest epigenome-wide meta-analysis on well-being to date. Although it identified no epigenome-wide significant methylation sites, it provided foundational insights for future research and underscored the need for larger, harmonized epigenetic datasets.
The final phase focused on synthesizing these insights through machine learning amodeling. By integrating EMA, SMTM, and exposomic data, the project worked toward a dynamic, personalized model of well-being. These models aim to identify individualized well-being trajectories and inform tailored interventions.
Innovative uses of language data expanded the toolkit for measuring well-being, especially across diverse populations and languages. The validation of SMTM as a complementary approach to traditional surveys opens new pathways for real-time, large-scale well-being monitoring.
As a lasting contribution, the Xplorit Foundation ensures continued societal translation of the project’s findings, facilitating collaborations across sectors to promote well-being at scale.
In sum, the WELL-BEING project has advanced the science of human well-being by delivering groundbreaking methodologies, integrated datasets, and actionable insights—laying a durable foundation for future interventions, policy, and research.